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General physical scene understanding requires more than simply localizing and recognizing objects -- it requires knowledge that objects can have different latent properties (e.g., mass or elasticity), and that those properties affect the outcome of physical events.
The visual discrimination of bending
J Norman, Elizabeth Wiesemann, Hideko Norman, M Taylor, and Warren Craft · 2007
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Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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Vision meets robotics: The kitti dataset
Andreas Geiger, Philip Lenz, Christoph Stiller, and Raquel Urtasun · 2013
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Human3. 6m: Large scale datasets and predictive methods for 3d human sensing in natural environments
Catalin Ionescu, Dragos Papava, Vlad Olaru, and Cristian Sminchisescu · 2013
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Seeing liquids from visual motion
Takahiro Kawabe, Kazushi Maruya, Roland Fleming, and Shin’ya Nishida · 2014
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Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2014
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Seeing liquids from static snapshots
Vivian Paulun, Takahiro Kawabe, Shin’ya Nishida, and Roland Fleming · 2015
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Galileo: Perceiving physical object properties by integrating a physics engine with deep learning
Jiajun Wu, Ilker Yildirim, Joseph J Lim, Bill Freeman, and Josh Tenenbaum · 2015
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Inferring mass in complex scenes by mental simulation
Jessica B. Hamrick, Peter W. Battaglia, Thomas L. Griffiths, and Joshua B. Tenenbaum · 2016
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Seeing jelly: Judging elasticity of a transparent object
Takahiro Kawabe and Shin’ya Nishida · 2016
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Learning to poke by poking: Experiential learning of intuitive physics
Pulkit Agrawal, Ashvin V Nair, Pieter Abbeel, Jitendra Malik, and Sergey Levine · 2016
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Learning visual predictive models of physics for playing billiards
Katerina Fragkiadaki, Pulkit Agrawal, Sergey Levine, and Jitendra Malik · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Shape, motion, and optical cues to stiffness of elastic objects
Vivian Paulun, Filipp Schmidt, Jan Assen, and Roland Fleming · 2017
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Deep visual foresight for planning robot motion
Chelsea Finn and Sergey Levine · 2017
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Intphys: A framework and benchmark for visual intuitive physics reasoning
Ronan Riochet, Mario Ynocente Castro, Mathieu Bernard, Adam Lerer, Rob Fergus, Véronique Izard, and Emmanuel Dupoux · 2018
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Shapestacks: Learning vision-based physical intuition for generalised object stacking
Oliver Groth, Fabian B Fuchs, Ingmar Posner, and Andrea Vedaldi · 2018
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Neurocomputational Modeling of Human Physical Scene Understanding
Ilker Yildirim, Kevin A. Smith, Mario Belledonne, Jiajun Wu, and Joshua B. Tenenbaum · 2018
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Visual features in the perception of liquids
Jan Assen, Pascal Barla, and Roland Fleming · 2018
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Neural material: Learning elastic constitutive material and damping models from sparse data
Bin Wang, Paul Kry, Yuanmin Deng, Uri Ascher, Hui Huang, and Baoquan Chen · 2018
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Learning particle dynamics for manipulating rigid bodies, deformable objects, and fluids
Yunzhu Li, Jiajun Wu, Russ Tedrake, Joshua B Tenenbaum, and Antonio Torralba · 2018
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Clevrer: Collision events for video representation and reasoning
Kexin Yi, Chuang Gan, Yunzhu Li, Pushmeet Kohli, Jiajun Wu, Antonio Torralba, and Joshua B Tenenbaum · 2019
Threedworld: A platform for interactive multi-modal physical simulation
Chuang Gan, Jeremy Schwartz, Seth Alter, Martin Schrimpf, James Traer, Julian De Freitas, Jonas Kubilius, Abhishek Bhandwaldar, Nick Haber, Megumi Sano, Kuno Kim, Elias Wang, Damian Mrowca, Michael Lingelbach, Aidan Curtis, Kevin T. Feigelis, Daniel M. Bear, Dan Gutfreund, David D. Cox, James J. DiCarlo, Josh H. McDermott, Joshua B. Tenenbaum, and Daniel L. K. Yamins · 2020
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Physion: Evaluating physical prediction from vision in humans and machines
Daniel M. Bear, Elias Wang, Damian Mrowca, Felix J. Binder, Hsiau-Yu Fish Tung, R. T. Pramod, Cameron Holdaway, Sirui Tao, Kevin A. Smith, Fan-Yun Sun, Li Fei-Fei, Nancy Kanwisher, Joshua B. Tenenbaum, Daniel L. K. Yamins, and Judith E. Fan · 2021
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Intphys 2019: A benchmark for visual intuitive physics understanding
Ronan Riochet, Mario Ynocente Castro, Mathieu Bernard, Adam Lerer, Rob Fergus, Véronique Izard, and Emmanuel Dupoux · 2021
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Dynamic visual reasoning by learning differentiable physics models from video and language
Mingyu Ding, Zhenfang Chen, Tao Du, Ping Luo, Josh Tenenbaum, and Chuang Gan · 2021
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Robonet: Large-scale multi-robot learning
Sudeep Dasari, Frederik Ebert, Stephen Tian, Suraj Nair, Bernadette Bucher, Karl Schmeckpeper, Siddharth Singh, Sergey Levine, and Chelsea Finn · 2019
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Cophy: Counterfactual learning of physical dynamics
Fabien Baradel, Natalia Neverova, Julien Mille, Greg Mori, and Christian Wolf · 2019
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Modeling human intuitions about liquid flow with particle-based simulation
Christopher J. Bates, Ilker Yildirim, Joshua B. Tenenbaum, and Peter Battaglia · 2019
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Learning latent dynamics for planning from pixels
Danijar Hafner, Timothy Lillicrap, Ian Fischer, Ruben Villegas, David Ha, Honglak Lee, and James Davidson · 2019
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Dream to control: Learning behaviors by latent imagination
Danijar Hafner, Timothy P. Lillicrap, Jimmy Ba, and Mohammad Norouzi · 2019
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Monet: Unsupervised scene decomposition and representation
Christopher P Burgess, Loic Matthey, Nicholas Watters, Rishabh Kabra, Irina Higgins, Matt Botvinick, and Alexander Lerchner · 2019
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Esprit: Explaining solutions to physical reasoning tasks
Nazneen Fatema Rajani, Rui Zhang, Yi Chern Tan, Stephan Zheng, Jeremy Weiss, Aadit Vyas, Abhijit Gupta, Caiming Xiong, Richard Socher, and Dragomir Radev · 2020
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gradsim: Differentiable simulation for system identification and visuomotor control
Krishna Murthy Jatavallabhula, Miles Macklin, Florian Golemo, Vikram Voleti, Linda Petrini, Martin Weiss, Breandan Considine, Jerome Parent-Levesque, Kevin Xie, Kenny Erleben, Liam Paull, Florian Shkurti, Derek Nowrouzezahrai, and Sanja Fidler · 2021
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Fitvid: Overfitting in pixel-level video prediction
Mohammad Babaeizadeh, Mohammad Taghi Saffar, Suraj Nair, Sergey Levine, Chelsea Finn, and Dumitru Erhan · 2021
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Training data-efficient image transformers & distillation through attention
Hugo Touvron, Matthieu Cord, Matthijs Douze, Francisco Massa, Alexandre Sablayrolles, and Hervé Jégou · 2021
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Datasheets for datasets
Timnit Gebru, Jamie Morgenstern, Briana Vecchione, Jennifer Wortman Vaughan, Hanna Wallach, Hal Daumé Iii, and Kate Crawford · 2021
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Comphy: Compositional physical reasoning of objects and events from videos
Zhenfang Chen, Kexin Yi, Yunzhu Li, Mingyu Ding, Antonio Torralba, Joshua B Tenenbaum, and Chuang Gan · 2022
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An approximate representation of objects underlies physical reasoning
Yichen Li, YingQiao Wang, Tal Boger, Kevin Smith, Samuel J Gershman, and Tomer Ullman · 2022
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Intuitive physics learning in a deep-learning model inspired by developmental psychology
Luis S Piloto, Ari Weinstein, Peter Battaglia, and Matthew Botvinick · 2022
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Masked conditional video diffusion for prediction, generation, and interpolation
Vikram Voleti, Alexia Jolicoeur-Martineau, and Christopher Pal · 2022
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Slotformer: Unsupervised visual dynamics simulation with object-centric models
Ziyi Wu, Nikita Dvornik, Klaus Greff, Thomas Kipf, and Animesh Garg · 2022
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Davit: Dual attention vision transformers
Mingyu Ding, Bin Xiao, Noel Codella, Ping Luo, Jingdong Wang, and Lu Yuan · 2022
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Simple unsupervised object-centric learning for complex and naturalistic videos
Gautam Singh, Yi-Fu Wu, and Sungjin Ahn · 2022
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Vdt: An empirical study on video diffusion with transformers
Haoyu Lu, Guoxing Yang, Nanyi Fei, Yuqi Huo, Zhiwu Lu, Ping Luo, and Mingyu Ding · 2023
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